Reinforcement Learning Helps Slam: Learning to Build Maps

Botteghi, Nicolò; Sırmaçek, Beril; Schulte, R.; Poel, Mannes; Brüne, Christoph

doi:10.5194/isprs-archives-xliii-b4-2020-329-2020

Cited by 11 publications

(6 citation statements)

References 26 publications

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“…DRL planner contribution: Without predictor, mapping using PPO is equal and even better comparing to the frontierbased method, in 10 to 50 percents (for completed episodes), while the difference is higher for the well ordered dataset D 1 and lower for D 2 . These results seams to improve the reported results in [8] [9] [10] where RL was nearly optimal. Yet, the predictor's contribution is the primary and DRL's contribution is the secondary.…”

Section: Simulation Results and Analysissupporting

confidence: 78%

“…Two conceptually different reward functions can be used: one that yields all the reward at the end of an episode (sparse) or one that provides a series of temporal rewards. The authors of [10] compared these two options and concluded that the second is preferable. This is not surprising, since rewarding momentary partial exposures encourages the agent to expose more cells, hence speeding up the learning process.…”

Section: B Reward Functionmentioning

confidence: 99%

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Integrating Deep Reinforcement and Supervised Learning to Expedite Indoor Mapping

Zwecher¹,

Iceland²,

R.³

et al. 2021

Preprint

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The challenge of mapping indoor environments is addressed. Typical heuristic algorithms for solving the motion planning problem are frontier-based methods, that are especially effective when the environment is completely unknown. However, in cases where prior statistical data on the environment's architectonic features is available, such algorithms can be far from optimal. Furthermore, their calculation time may increase substantially as more areas are exposed. In this paper we propose two means by which to overcome these shortcomings. One is the use of deep reinforcement learning to train the motion planner. The second is the inclusion of a pre-trained generative deep neural network, acting as a map predictor. Each one helps to improve the decision making through use of the learned structural statistics of the environment, and both, being realized as neural networks, ensure a constant calculation time. We show that combining the two methods can shorten the mapping time, compared to frontier-based motion planning, by up to 75%.

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Section: Simulation Results and Analysissupporting

confidence: 78%

Section: B Reward Functionmentioning

confidence: 99%

Integrating Deep Reinforcement and Supervised Learning to Expedite Indoor Mapping

Zwecher¹,

Iceland²,

R.³

et al. 2021

Preprint

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“…By integrating the techniques such as computer vision, deep learning, and reinforcement learning, UAV-based SLAM can achieve more advanced and complex tasks, such as autonomous navigation, object manipulation, and so forth. Several studies (Botteghi et al, 2020;Wen, Zhao, et al, 2020) with SLAM (Aslan et al, 2022;Cheein et al, 2011;Krul et al, 2021).…”

Section: Strengths and Limitationsmentioning

confidence: 99%

UAV‐based simultaneous localization and mapping in outdoor environments: A systematic scoping review

Wang,

Kooistra,

Pan

et al. 2024

Journal of Field Robotics

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This study aims to investigate the current knowledge of unmanned aerial vehicle (UAV)‐based simultaneous localization and mapping (SLAM) in outdoor environments and to discuss challenges and limitations in this field. A literature search was conducted in three online databases (Web of Science, Scopus, and IEEE) for articles published before October 2022 related to UAV‐based SLAM. A scoping review was carried out to identify the key concepts and applications, and discover research gaps in the use of algorithm‐oriented and task‐oriented, open‐source studies. A total of 97 studies met the criteria after conducting a two‐step screening by a systematic method followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses. Among eligible studies, 97 were classified into two main categories: algorithm‐oriented studies and task‐oriented studies. The analysis of the literature revealed that the majority of the studies were focused on the development and implementation of new algorithms and algorithms. This review highlights the significance and diversity of sensors utilized in UAVs in different tasks and applications scenarios that employ different types of sensors. The evaluation method is able to show the real results and performance of the new algorithms in the target scenarios compared with the evaluation method by the public data set and simulation platform.

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“…We note that, for such sensor-based path planning and/or target detection problems, machine learning-based methods have been on the agenda recently. In particular, reinforcement learning (RL) approach appears to be gaining popularity as an adaptive optimization methodology [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A reinforcement learning (RL)-based hybrid method for ground penetrating radar (GPR)-driven buried object detection

Alpdemir,

Sezgin

2024

Neural Comput & Applic

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Ground penetrating radar (GPR) systems are effective sensors for discovering various types of objects buried underground, such as military mines, metal objects, and pieces of underground infrastructures. A GPR system can be manually operated by a human or can be an integral part of a host platform. The host platform may be semi- or fully autonomous and may operate in different environments such as land vehicles or more recently air-borne drones. One challenge for the fully or semi-autonomous host platforms in particular is to find an efficient search procedure that would reduce the operation time and optimize resource utilization. Most of the current approaches are based on pre-defined search patterns which, for large and sparse areas, could mean unnecessary waste of time and resources. In this paper, we introduce a method that combines a coarse and therefore relatively low cost initial search pattern with a Reinforcement Learning (RL) driven efficient navigation path for eventual target detection, by exploiting the signal processing pipeline of the onboard GPR. We illustrate the applicability of the method using a well-known, high fidelity GPR simulation environment and a novel RL framework. Our results suggest that combination of a coarse navigation scheme and an RL-based training procedure based on GPR scan returns can lead to a more efficient target discovery procedure for host platforms.

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Reinforcement Learning Helps Slam: Learning to Build Maps

Cited by 11 publications

References 26 publications

Integrating Deep Reinforcement and Supervised Learning to Expedite Indoor Mapping

Integrating Deep Reinforcement and Supervised Learning to Expedite Indoor Mapping

UAV‐based simultaneous localization and mapping in outdoor environments: A systematic scoping review

A reinforcement learning (RL)-based hybrid method for ground penetrating radar (GPR)-driven buried object detection

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